Agent-based models are often complex and multidimensional

Typical model analyses, such as

• parameter screenings
• sensitivity analysis
• Population viability analysis

… often require many runs with hundreds of different parameter combinations and a high computational effort!

Some NetLogo tools already exist, each with its own caveats:

• NetLogo GUI /NetLogo Code
  • Not efficient
• NetLogo BehaviorSpace
  • Limitations
• BehaviorSearch
  • Only optimization
• RNetLogo (+ nlexperiment)
  • rJava issues
  • Setup needs lots of code

• Run experiments directly from R
• Reproducible workflow
• Easy setup
• Intuitive and simple workflow
• Many experiment setup methods already builtin
• Setup methods can be easily extended with own methods
• Basic output analysis functions available for most experiment setup methods
• Supports parallelisation, even on remote HPC machines

• Install via CRAN:
  install.packages("nlrx")
• Install via github:
  devtools::install_github("ropensci/nlrx")
• Online documentation:
  https://github.com/ropensci/nlrx
  https://ropensci.github.io/nlrx/
• Methods in Ecology and Evolution publication:
  https://doi.org/10.1111/2041-210X.13286

• nlrx is also useful to collect agent related spatial data
• such data can be used to create animated figures with enhanced visualization options (ggplot2, gganimate)

• Please report bugs and problems at our github

• We are also very open to contributions and ideas to improve nlrx further

• Big thanks to rOpenSci for reviewing and hosting our package!